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Two Postdoctoral Fellow Positions in AI-Driven Computational Biology
Suzhou Institute of Systems Medicine (ISM)

Two Postdoctoral Fellow Positions in AI-Driven Computational Biology

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Yang Zhang Lab | Center for AI and Computational Biology, Suzhou Institute of Systems Medicine (ISM), Chinese Academy of Medical Sciences & Peking Union Medical College (Tsinghua University School of Medicine), Suzhou, China 

The Yang Zhang Lab (https://zhanggroup.org) invites applications for two postdoctoral fellowships in the fields of artificial intelligence and computational biology, based at the Suzhou Institute of Systems Medicine (ISM) — a premier national hub for interdisciplinary research in systems medicine and AI-powered healthcare. These positions are part of a collaborative program that integrates research activities across Zhang Lab’s teams at both ISM and the National University of Singapore (NUS). Computational infrastructure and project frameworks are fully shared between the two locations. 

About the Lab 

The Yang Zhang Lab specializes in developing novel AI/machine learning algorithms for: 

  • Protein and RNA 3D structure prediction 
  • De novo protein and peptide design 
  • Protein–ligand docking and drug screening 
  • AI-driven cancer therapeutic development 

Our lab is internationally recognized for creating widely adopted tools such as I-TASSER, TM-score, and TM-align, supporting tens of thousands of biomedical studies worldwide. Learn more: https://zhanggroup.org 

Position Overview 

We are seeking highly motivated, creative, and independent postdoctoral researchers to join our interdisciplinary team. Applicants with strong interests in one or more of the following areas are encouraged to apply: 

  • AI and deep learning for biological data 
  • Protein/RNA folding and structural prediction 
  • De novo protein or peptide design 
  • Molecular docking, virtual screening, drug discovery 
  • Novel algorithms in computational oncology 

The focus of these projects is methodological innovation, not merely the application of existing tools. Candidates passionate about new algorithm design and its impact on cancer medicine will thrive in our environment. 

Qualifications: 

  • PhD in Computer Science, Computational Biology, Structural Biology, Biophysics, AI/ML, or related fields
  • Strong programming skills (e.g., Python, C/C++, scientific computing) 
  • Demonstrated creativity in algorithm or model development 
  • Proven publication track record (first-author papers in top-tier journals/conferences) 
  • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) 
  • Background in structural modeling, bioinformatics, or cheminformatics is a plus • Strong communication skills and ability to collaborate in a multidisciplinary team 

What We Offer

  • Internationally competitive salary and benefits 
  • Shared access to high-performance computing resources, including H100 GPU clusters 
  • Supportive mentorship with opportunities for first-author, high-impact publications 
  • Collaborative environment across ISM and NUS research programs 
  • Suzhou’s rich cultural heritage and modern living, ideal for focused research and family life 

Application Instructions

To apply, please submit a single PDF containing: 

  1. Curriculum Vitae, including full publication list 
  2. 1–2 page research statement outlining prior work and future goals 
  3. Contact details for three professional references

Email your application to Prof. Yang Zhang at [email protected]. Review of applications will begin immediately and continue until the positions are filled. Shortlisted candidates will be invited for online interviews.

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Jobbeskrivelse

Titel
Two Postdoctoral Fellow Positions in AI-Driven Computational Biology
Beliggenhed
100 Chongwen Road, Suzhou Industrial Park, Jiangsu Province Suzhou, Kina
Publiceret
2025-05-20
Ansøgningsfrist
Unspecified
Jobtype
Gem job

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Om arbejdsgiveren

Institute of Systems Medicine, Chinese Academy of Medical Sciences /Suzhou Institute of Systems Medicine (ISM) is a new type of research institution.

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